from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
import tensorflow as tf
import numpy as np
import gradio as gr

# Load tokenizer dan model
tokenizer = AutoTokenizer.from_pretrained("jeanetrixsiee/bert-finetuned-keras")
model = TFAutoModelForSequenceClassification.from_pretrained("jeanetrixsiee/bert-finetuned-keras")

# Label prediksi (urutan sesuai saat fine-tuning ya!)
labels = ['Negative', 'Neutral', 'Positive', 'Very Negative', 'Very Positive']

def predict_sentiment(text):
    inputs = tokenizer(text, return_tensors="tf", truncation=True, padding=True, max_length=256)
    logits = model(inputs)[0]
    probs = tf.nn.softmax(logits, axis=1).numpy()[0]
    pred_label = labels[np.argmax(probs)]
    confidence = probs[np.argmax(probs)]
    return f"{pred_label} (confidence: {confidence:.2f})"

demo = gr.Interface(
    fn=predict_sentiment,
    inputs=gr.Textbox(label="Enter a comment"),
    outputs=gr.Textbox(label="Prediction"),
    title="Sentiment Classifier",
    description="Fine-tuned BERT using Hugging Face Transformers for sentiment analysis."
)

demo.launch()